Data Visualization: Power BI LInk
Summary
In today's data-driven world, businesses are increasingly relying on data analysis to understand customer behavior and improve decision-making. In this case study, we delved into two critical datasets: customer sales information and customer engagement information. The objective is to provide insights into customer behavior, specifically exploring whether there is a correlation between sales and engagement.
Introduction
The role of data analysis in modern business cannot be overstated. In this project, we collaborated with the customer engagement manager, Janet, who has acquired two essential datasets related to customer sales and engagement. Janet seeks to gain insights into these datasets and determine whether there exists a correlation between customer engagement and sales.
Project Goal
The primary goal of this project is to produce a comprehensive report that analyzes both customer sales and engagement datasets. Specifically, we aim to:
Explore the data for patterns and trends.
Assess the correlation between sales and customer engagement.
Provide actionable insights that can inform business strategies.
Methodology
Data Collection Data was collected from two spreadsheet sources and extracted using Power BI, preparing it for further processing and transformation.
Data Pre-processing Given the cleanliness of the data, minimal data cleaning was required. Tables were transformed into Fact and Dimensions tables, ready for data modeling through Dimensional Modeling.
Data Modelling After optimizing the data and ensuring compatibility for Power Pivot (Dimensional Modeling), datasets were connected, establishing 1:M relationships from Dimensional tables to Fact Tables.
Data Analysis Data Analysis eXpressions (DAX) were employed to build numeric and quantitative metrics for comprehensive data exploration.
Data Visualization Following proper data modeling and metric creation, data visualizations were developed using best practices to effectively convey insights.
Key Insights
Engagement Correlation Initial analysis indicates a strong correlation between emails received, emails opened, and link clicks. Notably, February, September, and December exhibit the lowest engagement for all three dimensions.
Correlation Strength A more in-depth look using the Overview page suggests a very high correlation between Links clicked and Emails received. Testing the degree of correlation reveals a coefficient (R^2) closer to 1 than -1, reinforcing the strong relationship.
Email Conversion Rates For every 100 emails received by customers, approximately 49 customers open the email, and 25 click the link. For every 100 emails opened, 50% result in a link click. This demonstrates a close correlation between emails received, emails opened, and link clicks. Additionally, email conversion to sale stands at 25% for emails received and 50% for emails opened.
Product Effectiveness The Networking Product exhibits the highest Average Sales per Link Click ($525.68) and the highest Total Sales ($545,656). Conversely, the Consulting product has the lowest Average Sales per Link Click at $464.11 and a Total Sales of $481,742.
Actionable Intelligence
The insights obtained from this case study have practical applications, including:
Tailoring marketing strategies based on engagement patterns.
Optimizing email campaigns to improve conversion rates.
Identifying product strengths and weaknesses to inform product development and marketing efforts.
Summary and Conclusion
This case study demonstrates the power of data analysis in uncovering valuable insights. By exploring customer sales and engagement data, we have not only identified a strong correlation between engagement metrics but also gained a deeper understanding of customer behavior and product performance. These insights can guide data-driven decision-making and strategies for improved customer engagement and sales growth.
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